2 citations · 2 across the 8 of their papers we have counts for
9 papers
Despite Instructions: Frontier Agents Improvise Covert Channels at Test Time
Jacob Dineen, Silei Ren, Muhao Chen +2
In security-sensitive applications, language-model agents are often required to coordinate without disclosing confidential information. Yet repeated interactions may also let ordin…
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval
Yanzhen Shen, Sihao Chen, Xueqiang Xu +3
While significant progress has been made with dual- and bi-encoder dense retrievers, they often struggle on queries with logical connectives, a use case that is often overlooked ye…
On Reference (In-)Determinacy in Natural Language Inference
Sihao Chen, Chaitanya Malaviya, Alex Fabrikant +4
We revisit the reference determinacy (RD) assumption in the task of natural language inference (NLI), i.e., the premise and hypothesis are assumed to refer to the same context when…
Contextualized Evaluations: Judging Language Model Responses to Underspecified Queries
Chaitanya Malaviya, Joseph Chee Chang, Dan Roth +3
Language model users often issue queries that lack specification, where the context under which a query was issued -- such as the user's identity, the query's intent, and the crite…
Conceptual and Unbiased Reasoning in Language Models
Ben Zhou, Hongming Zhang, Sihao Chen +5
Conceptual reasoning, the ability to reason in abstract and high-level perspectives, is key to generalization in human cognition. However, limited study has been done on large lang…
Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations
Sihao Chen, Hongming Zhang, Tong Chen +7
We introduce sub-sentence encoder, a contrastively-learned contextual embedding model for fine-grained semantic representation of text. In contrast to the standard practice with se…
Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?
Bangzheng Li, Ben Zhou, Fei Wang +3
Despite the recent advancement in large language models (LLMs) and their high performances across numerous benchmarks, recent research has unveiled that LLMs suffer from hallucinat…
What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception
Chaitanya Malaviya, Subin Lee, Dan Roth +1
Eliciting feedback from end users of NLP models can be beneficial for improving models. However, how should we present model responses to users so they are most amenable to be corr…
ExpertQA: Expert-Curated Questions and Attributed Answers
Chaitanya Malaviya, Subin Lee, Sihao Chen +3
As language models are adopted by a more sophisticated and diverse set of users, the importance of guaranteeing that they provide factually correct information supported by verifia…